In most countries we find many different, isolated health information systems, causing many information management challenges. Public Health Information System have seen an explosive and often uncoordinated growth over the last years. Modern information technology makes it less costly to implement ICT4D solutions, which can lead to a high diversity of solutions. A staggering example was the mHealth moratorium declaration of Uganda´s MoH in 2012, as a reaction to an avalanche of around 50 mHealth solutions that were implemented within the course of a few years. Most of these solutions were standalone approaches that did not share their data with the national systems and rarely were developed beyond pilot status.
This may lead to the conclusion, that all systems should be connected or that interoperability is an objective in itself. However DHIS2 is often employed in contexts, where infrastructure is weak, and where resources to run even basic systems reliably are scarce. Fragmentation is a serious problem in this context, however interoperability approaches can only resolve some of the fragmentation problems - and often interoperability approaches result in an additional layer of complexity.
Example: Complexity of Logistics solutions in Ghana
In the area of Logistics or Supply Chain Management, often a multitude of parallel, overlapping or competing software solutions can be found in a single country. As identified in a JSI study in 2012, eighteen (18!) different software tools were documented as being used within the public health supply chain in Ghana alone.
Systems interoperability therefore seems as one possibility to remove fragmentation and redundancies and give public health officers a concise and balanced picture from available data sources. However the effort of connecting many redundant software solutions would be very high and therefore seems questionable. In a first step, focus should be on reducing the number of parallel systems and identifying the most relevant systems, afterwards these relevant systems can be integrated.
On this background, we want to define the major objectives of DHIS2 integration approaches:
Calculation of indicators: Many indicators are based on numerators and denominators from different data sources. Examples include mortality rates, including some mortality data as numerator and population data as denominator, staff coverage and staff workload rates (human resource data, and population and headcount data), immunization rates, and the like. For the calculation, you need both the numerator and denominator data, and they should thus be integrated into a single data warehouse. The more data sources that are integrated, the more indicators can be generated from the central repository.
Reduce manual processing and entering of data: With different data at the same place, there is no need to manually extract and process indicators, or re-enter data into the data warehouse. Especially interoperability between systems of different data types (such as patient registers and aggregate data warehouse) allows software for subsystems to both calculate and share data electronically. This reduces the amount of manual steps involved in data processing, which increases data quality.
Reduce redundancies: Often overlapping and redundant data is being captured by the various parallel systems. For instance will HIV/AIDS related data elements be captured both by both multiple general counselling and testing programs and the specialized HIV/AIDS program. Harmonizing the data collection tools of such programs will reduce the total workload of the end users. This implies that such data sources can be integrated into DHIS2 and harmonized with the existing data elements, which involves both data entry and data analysis requirements.
Improve organizational aspects: If all data can be handled by one unit in the ministry of health, instead of various subsystems maintained by the several health programs, this one unit can be professionalized. With staff which sole responsibility is data management, processing, and analysis, more specialized skills can be developed and the information handling be rationalized.
Integration of vertical programs: The typical government health domain has a lot of existing players and systems. An integrated database containing data from various sources becomes more valuable and useful than fragmented and isolated ones. For instance when analysis of epidemiological data is combined with specialized HIV/AIDS, TB, financial and human resource data, or when immunization is combined with logistics/stock data, it will give a more complete picture of the situation.
DHIS2 can help streamlining and simplifying system architecture, following questions such as:What is the objective of the integration effort? Can DHIS2 help reduce the number of systems? Can an DHIS2 integration help provide relevant management information at a lower cost, at at higher speed and with a better data quality than the existing systems? Is DHIS2 the best tool to replace other systems, or is another fit-for-purpose solution that can interoperate with DHIS2 more appropriate? More practical information on defining these objectives can be found in STEP 1 of the 6-Step implementation guideline.